Workshop on Machine Learning Approaches in Combinatorics and Automata Theory – 2025

Combinatorics, Automata theory, and formal languages are vital parts of the AI and ML ecosystems. These all point back to automata theory, which has importance in machine learning since it provides a theoretical foundation for building algorithms that can learn and manage enormous quantities of data. Although formal languages are abstract mathematical constructions, they have real-world applications, such as natural language processing, where they enable parsing and the ability to ‘read’ human languages, giving expressive power to machine learning.

The theory of finite automata has long been regarded as a cornerstone of theoretical computer science, providing a rigorous yet elegant paradigm for understanding computing at its most fundamental level. From early work on regular languages to present applications in text processing, embedded systems, and artificial intelligence, finite automata have shown to be both fundamental and amazingly practical.

Theory of Automata, Combinatorics and Its Applications in Science and Engineering compiles a wide set of topics that bridge the gap between theory and application. Each piece of work investigates a different aspect of finite automata, spanning from classical constructs to cutting-edge implementations in real-world contexts. Researchers in this field aim to demonstrate not just the mathematical elegance of automata theory and combinatorics, but also its expanding importance in fields such as compiler design, natural language processing, network protocol analysis, and DNA computing.